AI-Powered Detection of Fraudulent Web Platforms Using Behavioral and Structural Analysis

Abstract

The online services have grown incredibly fast, and with such growth, also increases the fraudulent online services, such as phishing websites, email spoofing, internet domain, and over-the-phone frauds. These attacks take advantage of the vulnerability in the structure of Uniform Resource Locators (URLs), identity formats of the sender, domain registration system and numbering system in telecommunications. Such dynamic and emerging attacks are not usually that easily detected using traditional rule-based security mechanisms, which make use of fixed signatures and fixed patterns.  The study suggests an Artificial Intelligence (AI)-based system to identify fraudulent web platforms based on structural analysis and behavioral analysis of phone numbers, email address, and URLs. The system combines telecommunication metadata analysis to detect suspicious phone numbers, Domain Name System (DNS) and Mail Exchange (MX) record authentication to determine sender integrity and a Random Forest machine learning classifier to profile URL and email-based threats. An interface based in a Flask allows meeting the task of real-time threat scanning and prediction.  The model uses data preprocessing, feature engineering, model training, heuristic evaluation, and deployment. Results of trials performed on sample phishing data show high accuracy, precision, recall and F1-score, which are indicators of strong detection results. The framework is designed to be flexible to adapt to the new categories of cyber threats.  The results indicate that the suggested system may be effectively used to reinforce traditional cybersecurity defenses through aid of the strengths revealed in automated detection and minimization of the use of rule-based approaches that are considered to be quite static.